The present invention relates to an information processing apparatus, an information processing method, and a program.
As a method for predicting what a selection subject (e.g., a consumer) selects from among multiple options (e.g., multiple commercial products), there is known conjoint analysis using a logit model as described in, for example, Japanese Patent Application Publication Nos. 2013-109470, 2005-316756, 2001-175761, 2011-65504, 2006-85558, and O. Chapelle and Z. Harchaoui, “A Machine Learning Approach to Conjoint Analysis,” Advances in Neural Information Processing Systems 17, L. K. Saul, Y. Weiss, and L. Bottou, Eds., 2005, pp. 257-264. In this method, the features of options are vectorized to perform a logistic regression analysis of the preference vector of a targeted person on the features of options and an actually selected option in order to build a prediction model for estimating a preference of the selection subject to predict a selection object that the selection subject will select in the future.
Here, since the conjoint analysis using a conventional logit model is based on the premise of a situation in which an option matching a preference is selected from among options recognized by a selection subject, the options recognized by the selection subject need to be specified explicitly in learning data. However, in normal learning data such as purchase data, only a commercial product selected by a consumer is recorded without recording with which commercial product the consumer has compared the commercial product to select the commercial product, and this cannot lead to the prediction of a selection object after considering the options recognized by the selection subject.
Further, the consideration of the options by the selection subject may be influenced by an environment at the time of selection, such as the cognitive bias and/or a point-of-sale situation, as well as the features of and preferences to the options. However, in the conjoint analysis using the conventional logit model, such an influence of the environment at the time of selection is not considered, and this makes it difficult to estimate a precise preference of the selection subject by excluding the influence of the environment.